根据学生水平生成合适难度的钢琴视奏曲谱，提升练习效率。
Difficulty-Aware Score Generation for Piano Sight-Reading
- 引入难度预测辅助目标，增强模型对难度控制的响应能力。
- 生成曲谱的难度控制精度显著提升，专家评价更符合预期。
- 适合音乐教育科技、个性化学习系统开发者参考。
将学习材料适配学生水平是教育中的关键问题。在音乐训练中，视奏——即首次见到乐谱就能演奏——需要循序渐进且与水平匹配的练习。然而，手工制作合适难度的练习曲耗时费力。本文将此问题视为受控符号音乐生成任务，旨在生成具有特定难度的钢琴曲谱。传统控制方式依赖控制标记，但难以有效影响整体难度等全局属性。为此，我们引入一个难度预测的辅助优化目标，缓解了条件坍缩问题——即模型在缺乏显式监督时忽略控制信号的现象。该目标帮助模型学习与目标难度对齐的内部表示，实现更精确、可调节的曲谱生成。通过自动指标和专家评估验证，生成曲谱的难度控制更优，具备潜在教育价值。本方法为基于生成技术实现个性化音乐教育迈出关键一步。
原文摘要 · Abstract (English)
Adapting learning materials to the level of skill of a student is important in education. In the context of music training, one essential ability is sight-reading -- playing unfamiliar scores at first sight -- which benefits from progressive and level-appropriate practice. However, creating exercises at the appropriate level of difficulty demands significant time and effort. We address this challenge as a controlled symbolic music generation task that aims to produce piano scores with a desired difficulty level. Controlling symbolic generation through conditioning is commonly done using control tokens, but these do not always have a clear impact on global properties, such as difficulty. To improve conditioning, we introduce an auxiliary optimization target for difficulty prediction that helps prevent conditioning collapse -- a common issue in which models ignore control signals in the absence of explicit supervision. This auxiliary objective helps the model to learn internal representations aligned with the target difficulty, enabling more precise and adaptive score generation. Evaluation with automatic metrics and expert judgments shows better control of difficulty and potential educational value. Our approach represents a step toward personalized music education through the generation of difficulty-aware practice material.
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